Using machine learning to predict the complete degradation of accelerated damp heat testing in just 10% of the time

The ability to accurately predict the long-term performance of photovoltaic modules would have substantial benefits for the photovoltaic market. If we can precisely determine how new modules will perform after 25–30 years in the field, the reliability and bankability of photovoltaic systems will significantly increase. Keeping this target in mind, this study presents the first step towards achieving more cost-effective degradation monitoring. We develop machine learning models to predict the performance of photovoltaic modules at the end of 1,000 hours of damp heat tests after modules have only spent less than 10% of that time in damp heat conditions. Hence, we investigate the ability of unsupervised neural ordinary differential networks to model the entire dynamics of the degradation during a damp heat test using only the data that is collected in the first 10% of the process. The developed algorithms can significantly reduce the required time for damp heat tests and pave the way to transform the photovoltaic market

Contactless Series Resistance Imaging of Perovskite Solar Cells via Inhomogeneous Illumination

A contactless effective series resistance imaging method for large area perovskite solar cells that is based on photoluminescence imaging with non-uniform illumination is introduced and demonstrated experimentally. The proposed technique is applicable to partially and fully processed perovskite solar cells if laterally conductive layers are present. The capability of the proposed contactless method to detect features with high effective series resistance is validated by comparison with various contacted mode luminescence imaging techniques. The method can reliably provide information regarding the severeness of the detected series resistance through photo-excitation pattern manipulation. Application of the method to sub-cells in monolithic tandem devices, without the need for electrical contacting the terminals, appears feasible.Comment: 17 pages, 5 figure

Novel Hybrid Electrode Using Transparent Conductive Oxide and Silver Nanoparticle Mesh for Silicon Solar Cell Applications

AbstractTransparent conductive oxides (TCOs) have been widely used as the front electrodes for various solar cell structures, including heterojunction silicon wafer solar cells and the vast majority of thin-film solar cells. For heterojunction silicon wafer solar cells, the front TCO layer not only serves as a top electrode (by enhancing the lateral conductance of the underlying amorphous silicon film), but also as an antireflection coating. These requirements make it difficult to simultaneously achieve excellent conductivity and transparency, and thus only high-quality indium tin oxide (ITO) has as yet found its way into industrial heterojunction silicon wafer solar cells. For thin-film solar cells, in order to provide efficient lateral conductance of the charge carriers, normally a TCO layer of a few hundred nanometers thickness is used which impedes the optical transparency due to the enhanced free carrier absorption. To reduce the conflict between conductivity and transparency, and to separately engineer the electrical and optical properties, a hybrid electrode is proposed and fabricated by us which consists of a TCO layer (optical layer) and a silver nanoparticle mesh (electrical layer). This hybrid electrode is demonstrated to have a 10 times higher lateral conductance compared to a single TCO layer, while maintaining high light transmission in a wide wavelength range. Due to the excellent performance of the hybrid electrode, it is demonstrated that such an electrode is suitable for various solar cell structures

Automated analysis of internal quantum efficiency using chain order regression

Spectral analysis of internal quantum efficiency (IQE) measurements of solar cells is a powerful method to identify performance-limiting mechanisms in photovoltaic devices. This analysis is usually performed using complex curve-fitting methods to extract various electrical and optical performance parameters. As these traditional fitting methods are not easy to use and are often sensitive to measurement noise, many users do not utilize the full potential of the IQE measurements to provide the key properties of their solar cells. In this study, we propose a simplified approach to analyze IQE curves of silicon solar cells using machine learning models that are trained to extract valuable information regarding the cell's performance and decoupling the parasitic absorption of the anti-reflection coating. The proposed approach is demonstrated to be a powerful characterization tool for solar cells as machine learning unlocks the full potential of IQE measurements

Decoupling Bimolecular Recombination Mechanisms in Perovskite Thin Films Using Photoluminescence Quantum Yield

We present a novel analytical model for analysing the spectral photoluminescence quantum yield of non-planar semiconductor thin films. This model considers the escape probability of luminescence and is applied to triple-cation perovskite thin films with a 1-Sun photoluminescence quantum yield approaching 25%. By using our model, we can decouple the internal radiative, external radiative, and non-radiative bi-molecular recombination coefficients. Unlike other techniques that measure these coefficients separately, our proposed method circumvents experimental uncertainties by avoiding the need for multiple photoluminescence measurement techniques. We validate our model by comparing the extracted implied open-circuit voltage, effective luminescence escape probabilities, absorptivity, and absorption coefficient with values obtained using established methods and found that our results are consistent with previous findings. Next, we compare the implied 1-Sun radiative open-circuit voltage and radiative recombination current obtained from our method with literature values. We then convert the implied open-circuit voltage and implied radiative open-circuit voltage to the injection-dependent apparent-effective and apparent-radiative carrier lifetimes, which allow us to decouple the different recombination coefficients. Using this lifetime analysis, we predict the efficiency losses due to each recombination mechanism. Our proposed analytical model provides a reliable method for analysing the spectral photoluminescence quantum yield of semiconductor thin films, which will facilitate further research into the photovoltaic properties of these materials

Decoupling Bimolecular Recombination Mechanisms in Perovskite Thin Films Using Photoluminescence Quantum Yield

We present a novel analytical model for analysing the spectral photoluminescence quantum yield of non-planar semiconductor thin films. This model considers the escape probability of luminescence and is applied to triple-cation perovskite thin films with a 1-Sun photoluminescence quantum yield approaching 25%. By using our model, we can decouple the internal radiative, external radiative, and non-radiative bi-molecular recombination coefficients. Unlike other techniques that measure these coefficients separately, our proposed method circumvents experimental uncertainties by avoiding the need for multiple photoluminescence measurement techniques. We validate our model by comparing the extracted implied open-circuit voltage, effective luminescence escape probabilities, absorptivity, and absorption coefficient with values obtained using established methods and found that our results are consistent with previous findings. Next, we compare the implied 1-Sun radiative open-circuit voltage and radiative recombination current obtained from our method with literature values. We then convert the implied open-circuit voltage and implied radiative open-circuit voltage to the injection-dependent apparent-effective and apparent-radiative carrier lifetimes, which allow us to decouple the different recombination coefficients. Using this lifetime analysis, we predict the efficiency losses due to each recombination mechanism. Finally, by comparing several different thicknesses, we conclude that the non-radiative bimolecular recombination is likely caused by surface recombination. Our proposed analytical model provides a reliable method for analysing the spectral photoluminescence quantum yield of semiconductor thin films, which will facilitate further research into the photovoltaic properties of these materials.Comment: Main text: 11 figures, 7 tables Supplemental Material: 42 figures, 7 table

Surface Saturation Current Densities of Perovskite Thin Films from Suns-Photoluminescence Quantum Yield Measurements

We present a simple, yet powerful analysis of Suns-photoluminescence quantum yield measurements that can be used to determine the surface saturation current densities of thin film semiconductors. We apply the method to state-of-the-art polycrystalline perovskite thin films of varying absorber thickness. We show that the non-radiative bimolecular recombination in these samples originates from the surfaces. To the best of our knowledge, this is the first study to demonstrate and quantify non-linear (bimolecular) surface recombination in perovskite thin films

Electrical Characterization of Thermally Activated Defects in n-Type Float-Zone Silicon

Float-zone (FZ) silicon is usually assumed to be bulk defect-lean and stable. However, recent studies have revealed that detrimental defects can be thermally activated in FZ silicon wafers and lead to a reduction of carrier lifetime by up to two orders of magnitude. A robust methodology which combines different characterization techniques and passivation schemes is used to provide new insight into the origin of degradation of 1 Ω·cm n-type phosphorus doped FZ silicon (with nitrogen doping during growth) after annealing at 500 °C. Carrier lifetime and photoluminescence experiments are first performed with temporary room temperature surface passivation which minimizes lifetime changes which can occur during passivation processes involving thermal treatments. Temperature- and injection-dependent lifetime spectroscopy is then performed with a more stable passivation scheme, with the same samples finally being studied by deep level transient spectroscopy (DLTS). Although five defect levels are found with DLTS, detailed analysis of injection-dependent lifetime data reveals that the most detrimental defect levels could arise from just two independent single-level defects or from one two-level defect. The defect parameters for these two possible scenarios are extracted and discussed

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.</p

Implied Open‐circuit Voltage Imaging via a Single Bandpass Filter Method—Its First Application in Perovskite Solar Cells

A novel, camera-based method for direct implied open-circuit voltage (iV$_{OC}$) imaging via the use of a single bandpass filter (s-BPF) is developed for large-area photovoltaic solar cells and precursors. The photoluminescence (PL) emission is imaged using a narrow BPF with centre energy inside the high-energy tail of the PL emission, utilising the close-to-unity and nearly constant absorptivity of typical photovoltaic devices in this energy range. As a result, the exact value of the sample\u27s absorptivity within the BPF transmission band is not required. The use of an s-BPF enables a fully contactless approach to calibrate the absolute PL photon flux for spectrally integrated detectors, including cameras. The method eliminates the need for knowledge of the imaging system spectral response. Through an appropriate choice of the BPF centre energy, a range of absorber compositions or a single absorber with different surface morphologies, such as planar and textured, can be imaged, all without the need for additional detection optics. The feasibility of this s-BPF method is first validated. The relative error in iV$_{OC}$ is determined to be ≤1.5%. The method is then demonstrated on device stacks with two different perovskite compositions commonly used in single-junction and monolithic tandem solar cells